To address these challenges, we introduce a new problem setting called the zero-shot object detection. As illustrated in Fig. REF , instead of merely classifying images, our goal is to simultaneously detect and localize each individual instance of new object classes, even in the absence of any visual examples of those classes during the training phase. In this regard, we propose a new zero-shot detection protocol built on top of the ILSVRC - Object Detection Challenge [1]}. The resulting dataset is very demanding due to its large scale, diversity, and unconstrained nature, and also unique due to its leveraging on WordNet semantic hierarchy [2]}. Taking advantage of semantic relationships between object classes, we use the concept of `meta-classes'Meta-classes are obtained by clustering semantically similar classes. and introduce a novel approach to update the semantic embeddings automatically. Raw semantic embeddings are learned in an unsupervised manner using text mining and therefore they have considerable noise. Our optimization of the class embeddings proves to be an effective way to reduce this noise and learn robust semantic representations.
